Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "7" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 50 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 48 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459865 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.143061 | -1.321779 | 11.356302 | 7.417229 | 1.147497 | 0.635850 | 2.440373 | 9.701686 | 0.7178 | 0.7000 | 0.3581 | nan | nan |
| 2459864 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.032276 | -2.405635 | 1.151339 | -0.145204 | -0.093387 | 0.090465 | 1.780768 | 30.546739 | 0.6935 | 0.6696 | 0.4117 | nan | nan |
| 2459863 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.183118 | -1.319351 | 0.582823 | 0.372986 | -0.771191 | 0.283019 | 1.266770 | 14.690695 | 0.6879 | 0.6613 | 0.4015 | nan | nan |
| 2459862 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.509441 | -1.366690 | 0.625832 | -0.414436 | -0.502014 | -0.110402 | 0.176112 | 8.121194 | 0.6764 | 0.6882 | 0.4162 | nan | nan |
| 2459861 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.099446 | -0.751942 | 0.604471 | 0.578197 | -0.614074 | 0.427473 | 0.716083 | 15.755682 | 0.7063 | 0.6757 | 0.4121 | nan | nan |
| 2459860 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.279936 | -1.224811 | 0.605373 | -0.367049 | 0.605781 | 0.687023 | 0.810766 | 12.936915 | 0.7124 | 0.6716 | 0.4132 | nan | nan |
| 2459859 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.907150 | -0.930763 | 0.530214 | 0.547893 | -0.376320 | 0.542284 | 0.428850 | 7.382327 | 0.7159 | 0.6749 | 0.4081 | nan | nan |
| 2459858 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.854889 | -0.576090 | 0.609411 | 0.480509 | -0.465538 | 1.129557 | 0.809290 | 14.535296 | 0.7270 | 0.6820 | 0.4233 | 2.736710 | 2.592722 |
| 2459857 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.489275 | -0.872867 | 0.311337 | 0.135996 | 0.430370 | 0.979096 | 0.848622 | 4.565037 | 0.0868 | 0.0828 | 0.0133 | nan | nan |
| 2459856 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.305652 | -1.487090 | 0.349102 | -0.502249 | -0.325822 | 0.113022 | 0.588087 | 6.276518 | 0.7193 | 0.6942 | 0.4085 | 2.881931 | 2.720441 |
| 2459855 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.617118 | -1.863536 | 0.214979 | -0.753442 | -0.749963 | 0.185076 | 0.048331 | 7.140344 | 0.6959 | 0.7037 | 0.4496 | 3.005392 | 2.891065 |
| 2459854 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.604087 | -1.386179 | -0.243034 | -0.901806 | -0.895605 | 0.049504 | 0.276321 | 10.873425 | 0.7207 | 0.7410 | 0.4423 | 2.697843 | 2.544004 |
| 2459853 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.485003 | -1.162996 | -0.193636 | -0.975480 | -0.591569 | 0.089701 | 1.512155 | 15.191707 | 0.7420 | 0.6854 | 0.4306 | 2.922575 | 2.856343 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 7.57% | -0.458441 | -1.288225 | -0.226543 | -0.906065 | 0.221433 | 0.475255 | 1.949705 | 2.017203 | 0.8233 | 0.8270 | 0.2508 | 2.782838 | 2.879193 |
| 2459851 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.058043 | -1.442580 | -0.370594 | -0.921027 | -0.500640 | 0.845478 | 0.380851 | 17.339803 | 0.7384 | 0.7472 | 0.3581 | 3.086562 | 2.982905 |
| 2459849 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.018378 | -1.337139 | -0.187669 | -0.525338 | 0.348663 | 0.166604 | 0.638095 | 16.299940 | 0.7362 | 0.7400 | 0.3643 | 3.861710 | 3.510532 |
| 2459848 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.871105 | -1.164794 | -0.800371 | 0.429635 | -0.177500 | 1.462356 | 0.868358 | 12.176497 | 0.7177 | 0.7405 | 0.3805 | 2.999462 | 2.900424 |
| 2459847 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.791541 | -1.176165 | -0.774806 | 0.551802 | -0.179128 | 0.090923 | 0.283549 | 11.383624 | 0.7215 | 0.6768 | 0.4342 | 3.058823 | 2.859703 |
| 2459846 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.986230 | -0.982748 | -0.538319 | 0.603679 | -0.523192 | 0.160979 | 0.365094 | 6.664838 | 0.8355 | 0.6757 | 0.4985 | 3.058121 | 3.099226 |
| 2459845 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.340604 | -1.141851 | -0.687183 | 0.440322 | 0.333198 | 0.312294 | 1.478844 | 9.213283 | 0.7389 | 0.7471 | 0.3735 | 5.469141 | 5.367610 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.655124 | 1.825787 | -0.984828 | 0.915915 | 0.675981 | 1.183987 | 0.341985 | 3.370621 | 0.1025 | 0.1067 | 0.0209 | nan | nan |
| 2459843 | digital_ok | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | -0.117275 | -1.306462 | -1.149996 | -0.892427 | 0.071676 | 0.011803 | 0.481651 | 14.646545 | 0.7438 | 0.7423 | 0.3938 | 4.262610 | 4.020635 |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 14.494798 | 18.639942 | 96.842018 | 101.666527 | nan | nan | -29.231224 | -30.920487 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.565202 | -1.495971 | -0.039868 | -0.460567 | -0.667253 | 0.868917 | 0.380765 | 7.601533 | 0.0511 | 0.0537 | 0.0093 | 1.305727 | 1.291662 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0938 | 0.0644 | 0.0140 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.023014 | -0.472209 | -0.621317 | -0.931943 | 5.405208 | 6.810993 | 30.328178 | 35.403296 | 0.0943 | 0.0649 | 0.0138 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.788325 | 0.813348 | -0.880162 | -0.810022 | 13.818419 | 15.439660 | 30.757427 | 36.563631 | 0.0865 | 0.0645 | 0.0080 | nan | nan |
| 2459832 | digital_ok | 100.00% | 0.00% | 16.13% | 0.00% | 100.00% | 0.00% | -0.942873 | -1.117721 | -0.201153 | -0.651515 | -0.456023 | -0.899052 | 0.795148 | 8.134188 | 0.7478 | 0.4356 | 0.5761 | 2.836360 | 2.699891 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.027462 | 1.768013 | -0.669881 | -0.007159 | 0.220334 | -0.791950 | 0.732274 | 0.449882 | 0.0372 | 0.0403 | 0.0012 | nan | nan |
| 2459830 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.732917 | -1.321009 | -0.013168 | -0.450714 | 1.769550 | 1.522177 | 1.656352 | 14.052473 | 0.0542 | 0.0637 | 0.0127 | 1.283808 | 1.276457 |
| 2459829 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.704266 | -1.890707 | -0.164774 | -0.491524 | 0.143087 | 0.228500 | 3.283286 | 29.713488 | 0.0616 | 0.0638 | 0.0091 | 19.151250 | 12.037044 |
| 2459828 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.534263 | -0.946048 | 0.003253 | -0.483329 | 0.004711 | 0.762367 | 4.706579 | 42.021504 | 0.0593 | 0.0704 | 0.0144 | 1.312584 | 1.304109 |
| 2459827 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.113635 | -1.147840 | 0.875096 | 0.822608 | -0.005495 | 4.066729 | 1.265590 | 7.194386 | 0.0692 | 0.0643 | 0.0098 | 1.257870 | 1.256182 |
| 2459826 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.597462 | -0.579572 | -0.186226 | -0.613913 | 10.623472 | 9.786142 | 2.386569 | 4.956627 | 0.0694 | 0.0664 | 0.0124 | 1.112535 | 1.116409 |
| 2459824 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.217356 | -0.833285 | 0.036548 | -0.475561 | 8.440015 | 8.343996 | 10.873896 | 29.845771 | 0.0508 | 0.0562 | 0.0055 | 24.677699 | 19.834121 |
| 2459823 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.219268 | -1.044600 | 0.950737 | 0.787282 | 0.007621 | 0.347846 | -0.308359 | 6.011210 | 0.0511 | 0.0669 | 0.0093 | 1.249366 | 1.248400 |
| 2459822 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.284207 | -1.412943 | 0.688733 | 0.107489 | -0.268921 | 0.358208 | -0.677828 | 0.682278 | 0.0762 | 0.0750 | 0.0151 | 1.225029 | 1.217156 |
| 2459821 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.956706 | -1.013652 | 0.400197 | 0.208611 | 2.519120 | 3.271757 | 1.171723 | 2.045664 | 0.0794 | 0.0755 | 0.0164 | 1.231435 | 1.227849 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.513237 | -1.273292 | 0.303532 | -0.104124 | -0.041079 | 1.073969 | -0.039383 | 11.119925 | 0.7597 | 0.6595 | 0.4392 | 4.114971 | 4.888156 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.323986 | -1.493712 | 0.204885 | -0.168341 | 0.298157 | 0.258093 | 1.351403 | 2.696234 | 0.8000 | 0.6429 | 0.5238 | 1.880775 | 1.747764 |
| 2459816 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.102272 | -1.019148 | -0.118711 | -0.332840 | -0.487832 | 0.464190 | 0.518610 | 18.403619 | 0.8390 | 0.5827 | 0.6075 | 3.427020 | 3.444825 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.849538 | -1.133814 | -0.057536 | -0.077464 | -0.997266 | 0.578301 | 1.387095 | 11.788509 | 0.7877 | 0.6446 | 0.5305 | 3.648068 | 3.332572 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | ee Power | 11.356302 | 0.143061 | -1.321779 | 11.356302 | 7.417229 | 1.147497 | 0.635850 | 2.440373 | 9.701686 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 30.546739 | -2.405635 | -1.032276 | -0.145204 | 1.151339 | 0.090465 | -0.093387 | 30.546739 | 1.780768 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 14.690695 | -1.183118 | -1.319351 | 0.582823 | 0.372986 | -0.771191 | 0.283019 | 1.266770 | 14.690695 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 8.121194 | -1.509441 | -1.366690 | 0.625832 | -0.414436 | -0.502014 | -0.110402 | 0.176112 | 8.121194 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 15.755682 | -0.751942 | -1.099446 | 0.578197 | 0.604471 | 0.427473 | -0.614074 | 15.755682 | 0.716083 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 12.936915 | -1.279936 | -1.224811 | 0.605373 | -0.367049 | 0.605781 | 0.687023 | 0.810766 | 12.936915 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 7.382327 | -0.907150 | -0.930763 | 0.530214 | 0.547893 | -0.376320 | 0.542284 | 0.428850 | 7.382327 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 14.535296 | -0.576090 | -0.854889 | 0.480509 | 0.609411 | 1.129557 | -0.465538 | 14.535296 | 0.809290 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 4.565037 | -0.872867 | -0.489275 | 0.135996 | 0.311337 | 0.979096 | 0.430370 | 4.565037 | 0.848622 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 6.276518 | -1.305652 | -1.487090 | 0.349102 | -0.502249 | -0.325822 | 0.113022 | 0.588087 | 6.276518 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 7.140344 | -1.863536 | -1.617118 | -0.753442 | 0.214979 | 0.185076 | -0.749963 | 7.140344 | 0.048331 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 10.873425 | -1.386179 | -1.604087 | -0.901806 | -0.243034 | 0.049504 | -0.895605 | 10.873425 | 0.276321 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 15.191707 | -1.162996 | -1.485003 | -0.975480 | -0.193636 | 0.089701 | -0.591569 | 15.191707 | 1.512155 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 2.017203 | -0.458441 | -1.288225 | -0.226543 | -0.906065 | 0.221433 | 0.475255 | 1.949705 | 2.017203 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 17.339803 | -1.058043 | -1.442580 | -0.370594 | -0.921027 | -0.500640 | 0.845478 | 0.380851 | 17.339803 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 16.299940 | -1.018378 | -1.337139 | -0.187669 | -0.525338 | 0.348663 | 0.166604 | 0.638095 | 16.299940 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 12.176497 | -1.164794 | -0.871105 | 0.429635 | -0.800371 | 1.462356 | -0.177500 | 12.176497 | 0.868358 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 11.383624 | -1.176165 | -0.791541 | 0.551802 | -0.774806 | 0.090923 | -0.179128 | 11.383624 | 0.283549 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 6.664838 | -0.986230 | -0.982748 | -0.538319 | 0.603679 | -0.523192 | 0.160979 | 0.365094 | 6.664838 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 9.213283 | -1.141851 | -0.340604 | 0.440322 | -0.687183 | 0.312294 | 0.333198 | 9.213283 | 1.478844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 3.370621 | -0.655124 | 1.825787 | -0.984828 | 0.915915 | 0.675981 | 1.183987 | 0.341985 | 3.370621 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 14.646545 | -1.306462 | -0.117275 | -0.892427 | -1.149996 | 0.011803 | 0.071676 | 14.646545 | 0.481651 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Power | 101.666527 | 18.639942 | 14.494798 | 101.666527 | 96.842018 | nan | nan | -30.920487 | -29.231224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 7.601533 | -1.495971 | -1.565202 | -0.460567 | -0.039868 | 0.868917 | -0.667253 | 7.601533 | 0.380765 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 35.403296 | -0.472209 | -0.023014 | -0.931943 | -0.621317 | 6.810993 | 5.405208 | 35.403296 | 30.328178 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 36.563631 | 0.813348 | 0.788325 | -0.810022 | -0.880162 | 15.439660 | 13.818419 | 36.563631 | 30.757427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 8.134188 | -0.942873 | -1.117721 | -0.201153 | -0.651515 | -0.456023 | -0.899052 | 0.795148 | 8.134188 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Shape | 1.768013 | -0.027462 | 1.768013 | -0.669881 | -0.007159 | 0.220334 | -0.791950 | 0.732274 | 0.449882 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 14.052473 | -0.732917 | -1.321009 | -0.013168 | -0.450714 | 1.769550 | 1.522177 | 1.656352 | 14.052473 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 29.713488 | -1.890707 | -1.704266 | -0.491524 | -0.164774 | 0.228500 | 0.143087 | 29.713488 | 3.283286 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 42.021504 | -0.946048 | -0.534263 | -0.483329 | 0.003253 | 0.762367 | 0.004711 | 42.021504 | 4.706579 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 7.194386 | -1.113635 | -1.147840 | 0.875096 | 0.822608 | -0.005495 | 4.066729 | 1.265590 | 7.194386 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | ee Temporal Variability | 10.623472 | -0.579572 | -0.597462 | -0.613913 | -0.186226 | 9.786142 | 10.623472 | 4.956627 | 2.386569 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 29.845771 | -1.217356 | -0.833285 | 0.036548 | -0.475561 | 8.440015 | 8.343996 | 10.873896 | 29.845771 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 6.011210 | -1.044600 | -1.219268 | 0.787282 | 0.950737 | 0.347846 | 0.007621 | 6.011210 | -0.308359 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | ee Power | 0.688733 | -1.284207 | -1.412943 | 0.688733 | 0.107489 | -0.268921 | 0.358208 | -0.677828 | 0.682278 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Variability | 3.271757 | -1.013652 | -0.956706 | 0.208611 | 0.400197 | 3.271757 | 2.519120 | 2.045664 | 1.171723 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 11.119925 | -1.513237 | -1.273292 | 0.303532 | -0.104124 | -0.041079 | 1.073969 | -0.039383 | 11.119925 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 2.696234 | -1.323986 | -1.493712 | 0.204885 | -0.168341 | 0.298157 | 0.258093 | 1.351403 | 2.696234 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 18.403619 | -1.019148 | -1.102272 | -0.332840 | -0.118711 | 0.464190 | -0.487832 | 18.403619 | 0.518610 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Temporal Discontinuties | 11.788509 | -1.133814 | -0.849538 | -0.077464 | -0.057536 | 0.578301 | -0.997266 | 11.788509 | 1.387095 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | N02 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |